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to tumour tissue images have improved characterisation of cancer tumours in clinical routine. However, traditional machine learning models require annotated data and are limited in scope, while foundation
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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
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machine learning and AI for clinical decision support. Develop, train, and validate predictive and explainable models using large-scale clinical registry data. Work closely with clinical collaborators
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extensive experience with physics-guided modeling; strong interest in time series machine learning and the ambition to learn are what matter most. The results will support safer automation, fewer failure
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combines: Fluid dynamics and heat transfer (theory and experiments), Computational modeling, and Machine learning / computer vision for data analysis and pattern recognition. The goal is to improve
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Statistics is offering a postdoctoral scholarship within the project “Phase transition in aggregation processes and network models”. The scholarship is full time for two years with starting date 1 January
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, applied mathematics, or a closely related field, awarded no more than three years prior to the application deadline*. Documented research experience in machine learning, AI, or statistical modeling. Proven
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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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programming skills, for example, to compute machine learning models and AI systems understand theory driven models, e.g. activity travel behaviour modelling effectively formulate research questions and
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foreign degree in speech technology, computer graphics, machine learning, computational linguistics, or a related area. This eligibility requirement must be met no later than the time the employment